Senior Credit Risk Data Scientist

Prodigy Finance - who are we?

Founded in 2007, Prodigy Finance is an international student lender that has helped over 45,000+ international master's students attend the world’s top universities. To date, Prodigy has disbursed over $2.3 billion in funding to students from more than 150 countries. Prodigy Finance is fuelled by impact investors and other privately qualified entities who invest in tomorrow's leaders while earning a financial and social return. Prodigy’s borderless lending model enables students to apply for a loan based on their future earning potential and not just their current circumstances and credit history.

What will you do in the role?

As a Senior Data Scientist within the Credit Risk team, you will design, develop, and implement models that drive lending decisions and portfolio risk management.

You will work across the full modelling lifecycle — from problem definition and data exploration to model development, validation, and deployment within a modern cloud environment. The role combines deep credit risk expertise with hands-on data science and engineering, with a focus on building robust, production-ready models.

You will play a key role in shaping how we assess risk, forecast portfolio performance, and optimise credit strategies across our global lending platform.

What are some of the responsibilities of this role?

• Explore and understand internal and external data to gain valuable insights and to enrich our analysis and machine learning pipelines
• Develop and maintain credit risk models, including PD, LGD, and EAD
• Build and enhance cash flow and portfolio forecasting models
• Design and implement predictive models using Python and machine learning techniques
• Work with large, complex datasets to perform feature engineering and model optimisation
• Collaborate with data and engineering teams to deploy models in AWS (e.g., SageMaker)
• Support model monitoring, validation, and governance processes
• Analyse portfolio trends and provide insights to improve credit strategy and underwriting decisions
• Contribute to IFRS 9-style forecasting and risk reporting frameworks
• Communicate technical concepts and model outputs clearly to stakeholders

What would the ideal candidate be great at?

• Passionate about Data Science
• Analytical thinking capability; be logical, systematic, strategic and pragmatic
• Strong foundation in credit risk modelling (PD, LGD, EAD)
• Ability to translate business problems into practical, data-driven solutions
• Strong attention to detail, both quantitative and qualitative, can organize large amounts of data from disparate sources
• Mindfulness; be considerate of the implications of your work, really care about what you are doing and the impact of your contribution
• Advanced Python-based modelling and data analysis
• Building models that are not just theoretically sound, but deployable and scalable in production
• Working across both statistical modelling and machine learning approaches
• Developing forecasting and cash flow models for lending portfolios
• Navigating and working within cloud-based environments (AWS)
• Writing efficient, reliable SAS/SQL queries for large datasets
• Communicating insights clearly to both technical and non-technical stakeholders
• Operating independently and taking ownership of end-to-end modelling problems

Qualifications and experience

• Bachelor’s, Honour’s or Master’s degree in a quantitative field (e.g., Mathematics, Statistics, Actuarial Science, Engineering, Economics, or similar)
• 5+ years of experience in credit risk, data science, or quantitative modelling roles
• Proven experience developing PD, LGD, or EAD models
• Strong programming experience in Python (e.g., pandas, numpy, scikit-learn)
• Solid experience with SAS/SQL and working with large datasets
• Experience working with AWS-based tools, particularly SageMaker (or similar cloud platforms)
• Familiarity with version control tools (e.g., GitHub)
• Experience with portfolio forecasting, cash flow modelling, or IFRS 9 frameworks is advantageous
• Experience implementing models in a production environment (particularly SageMaker)
• Background in fintech, lending, or financial services preferred

Our Purpose: To provide equal access to life-changing education globally

Our Values:

• We are doing something big here: We are doing something life-changing in the world. Something that changes the status quo.
• Bigger than us: Our work here is bigger than us as individuals and our own egos. It’s about doing the best work of our lives in service of the greater good.
• Grow bravely together: We have a relentless desire to continuously improve and work together to evolve our business and ourselves. We are open to the new and always stay curious.
• Keep pushing forward: What we are doing is not always easy. We embrace the challenge. We never give up at the first hurdle. We always keep moving forward.